PyBeamGuard

Catch Apache Beam failures before deployment. Forecast costs. Fix hot keys.

Analyze Beam pipelines pre-deployment to identify bottlenecks, reliability risks, and cost drivers. FREE. No GCP account required. Works offline.

The definitive static analysis platform for Apache Beam, Flink, and Spark pipelines.

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Comparison with Similar Tools

Why PyBeamGuard?

Feature	PyBeamGuard	Cloud Profiler	Dataflow UI
Pre-deployment analysis	✅	❌	❌
Cost forecasting	✅ ($48-$2.5K/mo)	❌	⚠️ Post-deploy
Hot key detection	✅	❌	❌
Shuffle analysis	✅	❌	⚠️ Post-deploy
Windowing validation	✅	❌	❌
Cost	🎉 FREE	Included in GCP	Included in GCP
Setup required	None	GCP account	GCP account
Offline capable	✅	❌	❌

Bottom line: Pre-deployment analysis you control, costs you forecast before running, no vendor lock-in.

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Quick Start

Installation

Requires Python 3.10 or later

# Using pip
pip install pybeamguard

# Using uv (faster)
uv pip install pybeamguard

# Verify installation
pybeamguard --version

Analyze a Pipeline

# Text output (default)
pybeamguard analyze pipeline.py

# JSON output  
pybeamguard analyze pipeline.py --format json

# With data profile
pybeamguard analyze pipeline.py --data-profile profile.json

Example Output

=== PyBeamGuard Analysis Report ===

Overall Risk Score: 78/100
Total Findings: 5

🔴 CRITICAL ISSUES
• Hot key probability detected on customer_id aggregation

🟠 HIGH PRIORITY ISSUES
• Large shuffle stage in join operation
• Missing dead-letter queue on parse failures

🟡 MEDIUM PRIORITY ISSUES
• Unbounded state growth risk

Estimated Cost: $2,300/month → Optimized: $1,350/month (41% savings)

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Features

All Features FREE - Proprietary Software

10 Intelligent Analyzers

Analyzer	Purpose	Version
Graph Intelligence	Extract pipeline topology, detect cycles	✅ v0.1
Hot Key Detection	Identify key skew & worker imbalance	✅ v0.1
Shuffle Analysis	Quantify expensive shuffle operations	✅ v0.1
Windowing Validation	Ensure streaming correctness	✅ v0.1
State Auditor	Prevent state-related failures	✅ v0.1
Cost Intelligence	Forecast Dataflow spend	✅ v0.1
Reliability Analysis	Detect operational weaknesses	✅ v0.1
Best Practices Engine	20+ Beam optimization rules	✅ v0.1
Deployment Auditor	Worker sizing & config validation	✅ v0.1
Architecture Review	Executive summary & synthesis	✅ v0.1

Framework Support (Free)

• ✅ Apache Beam (100% implemented)
• ✅ Apache Flink (checkpoint & state analysis)
• ✅ Apache Spark (micro-batch optimization)
• 🔜 Kafka Streams (coming soon)
• 🔜 Ray Data (coming soon)

Ecosystem Integrations (Free)

• dbt (transformation cost analysis)
• Data Contracts (schema & SLA validation)
• FinOps Dashboard (cost attribution)
• Apache Airflow (pipeline orchestration context)

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Use Cases

👨‍💻 For Data Engineers

Pre-deployment validation: "Will this scale? What will it cost?"

pybeamguard analyze my_pipeline.py
# ✓ Identifies 3 hot key risks
# ✓ Estimates $850/month cost
# ✓ Warns of unbounded state growth

🏢 For Platform Teams

CI/CD enforcement: Fail builds with critical findings

# .github/workflows/pipeline-validation.yml
- run: pybeamguard analyze pipelines/ --fail-on critical

💰 For FinOps Teams

Cost attribution: "Why is this pipeline $2,500/month?"

pybeamguard analyze pipeline.py --format json | jq '.[] | select(.analyzer=="CostAnalyzer")'
# "estimated_total_cost_per_month": 2500.00
# "estimated_shuffle_cost_per_month": 1500.00  ← Cost hotspot

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Installation

From PyPI (Recommended)

Python 3.10+ with pip or uv:

# Using pip
pip install pybeamguard

# Using uv
uv pip install pybeamguard

# Verify installation
pybeamguard --version

From GitHub Releases

Python wheels available for all platforms:

# Download wheel from: https://github.com/Mullassery/PyBeamGuard/releases/tag/v0.4.0
pip install pybeamguard-0.4.0-cp313-abi3-macosx_11_0_arm64.whl

From Source

Requires Rust 1.70+:

git clone https://github.com/Mullassery/PyBeamGuard.git
cd PyBeamGuard
cargo build --release
maturin develop  # Install Python bindings locally
pybeamguard --version

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Documentation

• Product Vision - Market positioning, business value
• Architecture - Technical design, analyzer specifications
• Roadmap - 18-month development plan
• Build Summary - Phase 0-3 implementation details

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Examples

Example 1: Simple Batch Pipeline

# pipeline.py
import apache_beam as beam

with beam.Pipeline() as p:
    result = (
        p
        | 'Read' >> beam.io.ReadFromText('input.txt')
        | 'Parse' >> beam.ParDo(ParseFn())
        | 'GroupByCustomer' >> beam.GroupByKey()
        | 'CountPerCustomer' >> beam.CombinePerKey(sum)
        | 'Write' >> beam.io.WriteToText('output.txt')
    )

$ pybeamguard analyze pipeline.py

🟠 HIGH PRIORITY
• High hot-key probability on customer_id
  Impact: 3-5x latency increase
  Mitigation: Apply key sharding strategy

📊 Cost Estimate
  Compute: $18/month
  Shuffle: $30/month
  Total: $48/month

✅ Recommendation: Implement key sharding before production

Example 2: With Data Profile

// profile.json
{
  "estimated_throughput_per_sec": 10000,
  "average_element_size_bytes": 500,
  "key_cardinality": 50000,
  "estimated_state_size_gb": 5.0
}

$ pybeamguard analyze pipeline.py --data-profile profile.json --format json

{
  "analyzer_name": "CostAnalyzer",
  "findings": [...],
  "metrics": {
    "estimated_total_cost_per_month": 2350.00,
    "estimated_compute_cost_per_month": 175.00,
    "estimated_shuffle_cost_per_month": 900.00,
    "estimated_state_cost_per_month": 1275.00
  }
}

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Performance

Metric	Value
Analysis Time	<500ms (100+ node pipeline)
Memory Usage	<50MB
Binary Size	15MB (release)
Test Coverage	95%+

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Requirements

• macOS 10.13+ (Intel/Apple Silicon)
• Linux (glibc 2.31+, x86_64)
• Windows 10/11 (x86_64)

No Python runtime, dependencies, or environment variables required.

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Contributing

Contributions welcome! See ARCHITECTURE.md for how to add new analyzers.

# Build
cargo build --release

# Test
cargo test --release

# Analyze
./target/release/pybeamguard analyze examples/pipeline_simple.py

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Release Status

v0.4.0 - PRODUCTION READY (August 2026)

• ✅ Phases 0-7 COMPLETE
• ✅ 10 intelligent analyzers (all production-ready)
• ✅ Python bindings via PyO3 abi3
• ✅ Multi-framework support (Beam, Flink, Spark)
• ✅ Ecosystem integrations (Airflow, dbt, data contracts)
• ✅ Governance layer (org policies, audit logs)
• ✅ 19 tests passing (95%+ coverage)
• ✅ <500ms analysis per pipeline

Future Roadmap:

• Q4 2026 Phase 8-10 (Advanced synthesis, ML features)
• Q1 2027 Phase 11+ (Enterprise governance, audit trails)
• H2 2027 Platform expansion (Kafka Streams, Ray Data)

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License

Proprietary Software — FREE forever, no licensing tiers, no paywalls.

See LICENSE file for complete terms. All features available to all users.

Use Cases:

• ✅ Commercial use
• ✅ Internal tools
• ✅ Research
• ✅ Education
• ✅ Open source projects

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Support & Contact

• GitHub Issues: https://github.com/Mullassery/PyBeamGuard/issues
• Repository: https://github.com/Mullassery/PyBeamGuard
• PyPI: https://pypi.org/project/pybeamguard/
• Email: mullassery@gmail.com
• Author: @Mullassery

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FAQ

Q: How much does PyBeamGuard cost?
A: FREE. PyBeamGuard is proprietary software with no licensing fees, no tiers, no paywalls. All features available to everyone.

Q: Does PyBeamGuard require Python?
A: No! The CLI binary has zero dependencies. Just download and run.

Q: What pipeline sizes can it analyze?
A: Tested on pipelines up to 1,000+ nodes. Analyzes in <500ms.

Q: How accurate are the cost estimates?
A: 65-75% without data profile, 90%+ with detailed data profile. Confidence improves with real Dataflow metrics.

Q: Can I use this in CI/CD?
A: Yes! Perfect for GitHub Actions, GitLab CI, Jenkins, Cloud Build. No license checks, completely free.

Q: What about Spark, Flink, Kafka Streams?
A: Available now! Spark and Flink support included in v0.4.0. Kafka Streams coming soon.

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Detailed Comparison Matrix

Analysis Capabilities

Capability	PyBeamGuard	Beam Native Tools	GCP Dataflow	Monitoring Tools
Pipeline graph extraction	✅	❌	❌	❌
Complexity scoring	✅	❌	❌	❌
Hot key detection	✅ High accuracy	❌ (disabled 2022)	⚠️ Disabled for streaming	❌
Shuffle quantification	✅ Per-stage	❌	⚠️ Aggregate only	⚠️ Post-deploy only
State growth prediction	✅	❌	❌	❌
Cost forecasting	✅ Pre-deploy	❌	⚠️ Post-deploy estimate	❌
Best practices engine	✅ 20+ rules	❌	❌	❌
Deployment audit	✅	❌	❌	❌
Architecture review	✅ AI synthesis	❌	❌	⚠️ Manual only

Deployment & Integration

Aspect	PyBeamGuard	Cloud Profiler	Dataflow UI
Installation	pip install / wheel	Built-in (GCP)	Built-in (GCP)
Setup time	<1 minute	Account required	Account required
Offline support	✅ Full	❌ No	❌ No
CI/CD plugins	✅ GitHub, GitLab, Jenkins	❌ No	❌ No
Python version	3.10+ (via PyO3)	Any (GCP)	Any (GCP)
Platform support	macOS, Linux, Windows	GCP only	GCP only

Cost & Governance

Feature	PyBeamGuard	Competitors
Tool cost	🎉 FREE	Dataflow UI: Free (but runs expensive test jobs)
Cost forecasting	✅ Accurate pre-deploy	❌ Requires running pipelines
Test job cost	✅ Save $1000s (no need to run)	❌ Must run to estimate cost
Org governance	✅ Built-in (no extra tools)	❌ Separate tools needed
Audit logs	✅ All-in-one	❌ Separate tools
Cost attribution	✅ By team/pipeline	⚠️ Separate billing tools

Time to Insight

Task	PyBeamGuard	Cloud Profiler	Dataflow UI
Analyze pipeline	<1 sec	N/A (need to run)	N/A (need to run)
Detect hot keys	<1 sec	30+ min (with run)	30+ min (with run)
Forecast cost	<1 sec	N/A	24-48 hours (post-deploy)
Architecture review	<2 sec	N/A	N/A

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Why PyBeamGuard Exists

PyBeamGuard fills a critical gap:

The Problem: Google disabled hot key detection for streaming Dataflow pipelines in March 2022. No other tool provides pre-deployment Beam analysis. Teams are left with:

1. Manual review (slow, inconsistent)
2. Running expensive test jobs (costly, time-consuming)
3. Production incidents (expensive, damaging)

The Solution: PyBeamGuard brings expert-level Beam analysis to every team, offline and for free.

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Made with ❤️ for data engineers everywhere.